Abstract
The paper demonstrates the use of Bayesian networks in multicriteria decision analysis (MCDA) of environmental design alternatives for environmental flows (eflows) and physical habitat remediation measures in the Mandalselva River in Norway. We demonstrate how MCDA using multi-attribute value functions can be implemented in a Bayesian network with decision and utility nodes. An object-oriented Bayesian network is used to integrate impacts computed in quantitative sub-models of hydropower revenues and Atlantic salmon smolt production and qualitative judgement models of mesohabitat fishability and riverscape aesthetics. We show how conditional probability tables are useful for modelling uncertainty in value scaling functions, and variance in criteria weights due to different stakeholder preferences. While the paper demonstrates the technical feasibility of MCDA in a BN, we also discuss the challenges of providing decision-support to a real-world habitat remediation process.
Highlights
Environmental flow has been defined as ‘the hydrological regime required to sustain freshwater and estuarine ecosystems, and the human livelihoods and well-being that depend on them’ (Acreman et al, 2014)
One of the shortcomings of the study was not evaluating the potential for physical habitat restoration measures to partly substitute for stricter environmental flow requirements that follow from the application of the Water Framework Directive (WFD) in the revision process
We demonstrate how different quantitative and qualitative impact assessments can be combined in an Object-Oriented Bayesian Network (OOBN) with nodes for valuation functions, criteria weights and decision nodes, in order to consistently integrate uncertainty from different impact model domains within multicriteria decision analysis (MCDA)
Summary
Environmental flow (eflow) has been defined as ‘the hydrological regime required to sustain freshwater and estuarine ecosystems, and the human livelihoods and well-being that depend on them’ (Acreman et al, 2014). The model is deployed in an online interface to ease communication with a wider community To our knowledge this is the first example in Norway of MCDA integrating assessment of eflows and physical habitat restoration measures through the application of a Bayesian network approach. Reichert et al (201 ) discuss requirements for environmental decision support, arguing that a combination of probability theory and scenario planning with multi-attribute utility theory fulfills requirements for representation and quantification of scientific knowledge, elicitation of societal preferences and communication with authorities, politicians and the public They argue for explicit modelling of ecological state, as separate from other ecosystem services, in order to better account for complexity in valuation. We discuss the advantages and limitations of our application of MCDA in an OOBN
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